Imagine having x-ray vision for your application and seeing exactly what's happening under the hood in real time. That's what thread dumps do for your application—they are a vital component of application performance monitoring (APM) and give you a super-powered peek into its inner workings, helping you spot issues and fix them faster than you can imagine.

Elastic Universal Profiling™ agent is now open source! The industry’s most advanced fleetwide continuous profiling solution empowers users to identify performance bottlenecks, reduce cloud spend, and minimize their carbon footprint. This post explores the history of the agent, its move to open source, and its future integration with OpenTelemetry.

The topic of continuous profiling has been an ongoing discussion in the observability world for some time. I said back in 2021 that profiling was set to be the next major telemetry signal in observability, and in fact, since then there’s been growing interest in profiles. Startups and large observability vendors have gotten into this domain. A significant recent step was when the OpenTelemetry project decided to add profiles to its core signals and formalized the open unified specification for that.

On March 12 Google began promoting INP (Interaction to Next Paint) into a Core Web Vital metric in an effort to push performance beyond page loads. This means your website or application’s SEO ranking may be impacted if users do not have smooth interactions on the site or app. While this change is a net positive for users, finding the root cause of these reported slow interactions can be tricky for developers.

Profile-guided optimization (PGO) is a compiler feature that uses runtime profiling data to optimize code. Now fully integrated in Go 1.21+, PGO is a powerful tool to boost application performance — and with Grafana Pyroscope, our open source continuous profiling database, you can significantly magnify the value of PGO. In this post, we’ll explore what PGO is, how the Pyroscope team has used it internally to improve performance, and how you can use PGO to make your own programs faster.

A while ago, we posted a blog that detailed how we imported over 4 billion chess games with speed using Python and optimized the code leveraging our Universal ProfilingTM. This was based on Elastic Stack running on version 8.9. We are now on 8.12, and it is time to do a second part that shows how easy it is to observe compiled languages and how Elastic®’s Universal Profiling can help you determine the benefit of a rewrite, both from a cost and environmental friendliness angle.

Since enabling browser profiling on our Sentry.io dashboard a month ago, we have collected over 2M profiles and learned a lot about how our users experience our dashboard. The profiles collected gave us insight into how our dashboard performs in production and surfaced some issues causing UI jank. In this post, we will look at an example of an issue we discovered using Profiling.